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Title: Knowledge Graph based Learning Guidance for Cybersecurity Hands-on Labs
Hands-on practice is a critical component of cybersecurity education. Most of the existing hands-on exercises or labs materials are usually managed in a problem-centric fashion, while it lacks a coherent way to manage existing labs and provide productive lab exercising plans for cybersecurity learners. With the advantages of big data and natural language processing (NLP) technologies, constructing a large knowledge graph and mining concepts from unstructured text becomes possible, which motivated us to construct a machine learning based lab exercising plan for cybersecurity education. In the research presented by this paper, we have constructed a knowledge graph in the cybersecurity domain using NLP technologies including machine learning based word embedding and hyperlink-based concept mining. We then utilized the knowledge graph during the regular learning process based on the following approaches: 1. We constructed a web-based front-end to visualize the knowledge graph, which allows students to browse and search cybersecurity-related concepts and the corresponding interdependence relations; 2. We created a personalized knowledge graph for each student based on their learning progress and status; 3.We built a personalized lab recommendation system by suggesting more relevant labs based on students’ past learning history to maximize their learning outcomes. To measure the effectiveness of the proposed solution, we have conducted a use case study and collected survey data from a graduate-level cybersecurity class. Our study shows that, by more » leveraging the knowledge graph for the cybersecurity area study, students tend to benefit more and show more interests in cybersecurity area. « less
Authors:
; ; ; ;
Award ID(s):
1723440
Publication Date:
NSF-PAR ID:
10193689
Journal Name:
ACM Global Computing Education Conference (CompEd)
Page Range or eLocation-ID:
194 to 200
Sponsoring Org:
National Science Foundation
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